• Journal of Radiation Protection and Research
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• v.27 no.1
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• pp.59-66
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• 2002

The role of experiments in science education is essential for understanding the natural phenomena and principle related to a subject. Therefore, the remote control experiment via Internet is one of key solution for distance learners in science education. The remote experiments ate also necessary for the time-consuming experiment which takes several days, collaborative experiment between distance learners, expensive laboratory equipment which is not usually available to students, experimental procedure which is dangerous, etc. In this study, we have developed a general method for a remote control laboratory system using internet and interlace techniques. It is possible for students to learn the nuclear physics to control the real instruments and conduct physics experimentation with internet techniques. We proposed the remote control radiation measurement system as a sample application. This system could be useful for the monitoring near a nuclear power plants in order to improve the environment data credibility to the public.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1421-1427
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• 2018

Due to the rapid development of Internet-of-Things technology, different types of smart sensors are now devised and deployed widely. These smart sensors are now used in animal husbandry which was traditionally managed by the experience of farmers, such that wearable sensors for livestock, and the smart farm which is equipped with multiple sensors are utilized to increase the efficiency of livestock management. Herein, we consider a scheme in which the body temperature and the level of activity are measured by smart sensor which is attached to the neck of dairy cattle and the health condition is monitored based on collected data. Especially, we find that the estrous of dairy cattle which is one of most important metric in milk production, can be predicted with high precision using various machine learning techniques. By utilizing the proposed prediction scheme, estrous of cattle can be detected immediately and this can improve the efficiency of cattle management.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.3
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• pp.134-141
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• 2011

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.1-8
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• 2021

This study developed low-cost and high-efficiency pavement condition monitoring technology to produce the key information required for pavement management. A noise and artificial intelligence-based monitoring system was devised to compensate for the shortcomings of existing high-end equipment that relies on visual information and high-end sensors. From idea establishment to system development, functional definition, information flow, architecture design, and finally, on-site field evaluations were carried out. As a result, confidence in the high level of artificial intelligence evaluation was secured. In addition, hardware and software elements and well-organized guidelines on system utilization were developed. The on-site evaluation process confirmed that non-experts could easily and quickly investigate and visualized the data. The evaluation results could support the management works of road managers. Furthermore, it could improve the completeness of the technologies, such as prior discriminating techniques for external conditions that are not considered in AI learning, system simplification, and variable speed response techniques. This paper presents a new paradigm for pavement monitoring technology that has lasted since the 1960s.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• The Journal of Society for e-Business Studies
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• v.12 no.4
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• pp.71-84
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• 2007

The architecture for RFID-based home network and an intelligent agent for efficiently managing home network are proposed in this paper. The agent consists of six modules-Agent Manager, Data Collector, Execution Controller, Data Storage, Data Queue and User Interface. Agent Manager manages the tasks of modules, and Data Collector collects the data from home appliances through the RFID readersm Execution Controller determines the operations home appliances according to the conditions of the home environment and transfers the operations to the appliances through the RFID readers. Moreover, Data Storage keeps the data which is necessary for the operations of the agent, and Data Queue temporarily stores the data which is collected from home appliances. Lastly, User Interface provides the graphical user interface in which an individual can directly control and monitor the home network. The proposed intelligent agent autonomously learns the circumstances of a home network by analyzing the data about the state of home appliances, and controls home appliances according to the preference of the user. Therefore, the user can live in the best-suited home environment without direct appliance control if he/she performs home networking through the agent.